code: def test_deepspeech(self):
      n_batch = 2
      n_feature = 1
      n_channel = 1
      n_class = 40
      n_time = 320
  
      model = DeepSpeech(n_feature=n_feature, n_class=n_class)
  
      x = torch.rand(n_batch, n_channel, n_time, n_feature)
      out = model(x)
  
      assert out.size() == (n_batch, n_time, n_class)
  
imports: import itertools
  from collections import namedtuple
  import torch
  from parameterized import parameterized
  from torchaudio.models import ConvTasNet, DeepSpeech, Wav2Letter, WaveRNN
  from torchaudio.models.wavernn import MelResNet, UpsampleNetwork
  from torchaudio_unittest import common_utils
  from torchaudio_unittest.common_utils import torch_script
label_desc: Use image node_id 1 for calling the TestDeepSpeech obj's underlying member method code with example usage: obj.test_deepspeech() without return types